Ensemble of decision trees: XGBoost
Ensemble of decision trees: XGBoost Data Science Project
Classification in Depth with Scikit-Learn

Ensemble of decision trees: XGBoost

The focus of the project is on the XGBoost model, one of the most important machine learning ensemble models used in the industry. The project will cover the fundamentals of the model and provide practical experience in implementing it using the sklearn library.
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Ensemble of decision trees: XGBoostEnsemble of decision trees: XGBoost
Project Created by

Verónica Barraza

Project Activities

All our Data Science projects include bite-sized activities to test your knowledge and practice in an environment with constant feedback.

All our activities include solutions with explanations on how they work and why we chose them.

multiplechoice

Based on the plot, select the learning rate that gives the best performance on the validation set

multiplechoice

True or False: The following figure shows the decision boudaries of an XGBClassifier with a learning rate of 0.1

Use the code presented previously.

answer-lr01

multiplechoice

True or False: The following figure shows the decision boudaries of an XGBClassifier with a learning rate of 0.01

Use the code presented previously.

answer-lr0001

multiplechoice

Which of the following statements about learning rate and overfitting in XGBoost is true?

codevalidated

Train your model

You must train the XGBoost and tune hyperparameters model using the training data and evaluate the model using the testing data.

Store the model in the variable model, the prediction on the testing data in y_pred and the testing evalaution metrics in test_accuracy, test_precision, and test_recall.

To achive this task, you should obtain a precision of 70%.

Note: The expected evaluation metrics for a simple problem varies depending on the specifics of the problem and data.

Ensemble of decision trees: XGBoostEnsemble of decision trees: XGBoost
Project Created by

Verónica Barraza

This project is part of

Classification in Depth with Scikit-Learn

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